LLM
How to deploy Qwen3.5-122B-A10B on a GPU cloud
A 125B (MoE) language model for chat and instruction-following. Full specs, license and use cases.
Qwen3.5-122B-A10B size and hardware requirements
125.1B
Total parameters
~10B active per token (mixture-of-experts; see total parameters above)
Active parameters
BF16
Published precision
279.6 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 233.0 GB | 279.6 GB | RTX A6000 | 6 | $1.98/hr |
| FP8 (quantized) | 116.5 GB | 139.8 GB | RTX 4000 SFF Ada | 7 | $1.26/hr |
| INT4 (quantized) | 58.2 GB | 69.9 GB | A100 | 1 | $0.851/hr |
How to run Qwen3.5-122B-A10B
Run Qwen3.5-122B-A10B with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve Qwen/Qwen3.5-122B-A10B --tensor-parallel-size 6Run Qwen3.5-122B-A10B with Ollama
Verified against Ollama's own library listing.
ollama run qwen3.5:122bRun Qwen3.5-122B-A10B with GGUF quantizations
Prebuilt GGUF weights published at unsloth/Qwen3.5-122B-A10B-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/Qwen3.5-122B-A10B-GGUFSource: https://huggingface.co/unsloth/Qwen3.5-122B-A10B-GGUF
Deploy Qwen3.5-122B-A10B on Aquanode
Aquanode has no one-click deploy template for Qwen3.5-122B-A10B; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (6× RTX A6000 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod